Baseline

Load data

 Topo layer - jigsaw vector polygons

Highways layer - road centre lines

Topo lines - line segment jigsaw edges

Raster layer

NaPTAN - bus and other transport hub points

NaPTAN data Ealing busstops only

filteres in Excel - filters are incomplete

ebs = pd.read_csv("../data/NapTAN/StopsEaling.csv")

create geometry from Easting and Northing, BNG 27700

ebs = gpd.GeoDataFrame( ebs, geometry = gpd.points_from_xy(ebs.Easting, ebs.Northing), crs="EPSG:27700") print(ebs.geometry.name, ebs.crs)

write as spatial file

ebs.to_file("../data/NapTAN/StopsEaling.gpkg", layer = "stops", driver="GPKG") ebs.plot()

NaPTAN data all busstops

something weird about the filters, not getting all

stops in the are

read in whole lot and create gpkg

read all cols as string except specified cols

bs = pd.read_csv( "../data/NapTAN/Stops.csv" , dtype = str , converters = {'Easting': np.int64, 'Northing': np.int64 , 'Longitude': np.float64, 'Latitude': np.float64} )

create geometry from Easting and Northing, BNG 27700

bs = gpd.GeoDataFrame( bs, geometry = gpd.points_from_xy(bs.Easting, bs.Northing), crs="EPSG:27700") print(bs.geometry.name, bs.crs)

write as spatial file

bs.to_file("../data/NapTAN/Stops.gpkg", layer = "stops", driver="GPKG")

bs.plot()

Explore data

Plot road sections

Create some useful subsets of data

Baseline 1

To do

Roads in TQ1980 divided by parking space length

Feature engineering per road section TOID

Work towards dataframe with one row per road section TOID from TopoArea layer. For each roads section

MV idea

Identify road intersections by road node

Compactness measure: isoperimetric inequality

$4\pi A \leq L^2$ where $A$ is area of shape and $L$ is its perimeter length. equality holds only if shape is a circle.

The corresponding compactness quotient $Q = \frac{4\pi A}{L^2} \in [0,1]$ where values close to $1$ are more compact and closer in shape to a circle and values close to $0$ are less compact.

Plan

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